Notes#

Hide imports 
import os
from collections import defaultdict, Counter

from git import Repo
import dimcat as dc
import ms3
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

from utils import STD_LAYOUT, CADENCE_COLORS, CORPUS_COLOR_SCALE, chronological_corpus_order, color_background, get_corpus_display_name, get_repo_name, resolve_dir, value_count_df, get_repo_name, print_heading, resolve_dir
Hide source 
CORPUS_PATH = os.path.abspath(os.path.join('..', '..'))
ANNOTATED_ONLY = os.getenv("ANNOTATED_ONLY", "True").lower() in ('true', '1', 't')
print_heading("Notebook settings")
print(f"CORPUS_PATH: {CORPUS_PATH!r}")
print(f"ANNOTATED_ONLY: {ANNOTATED_ONLY}")
CORPUS_PATH = resolve_dir(CORPUS_PATH)

Notebook settings
-----------------

CORPUS_PATH: '/home/runner/work/workflow_deployment/romantic_piano_corpus'
ANNOTATED_ONLY: True
Hide source 
repo = Repo(CORPUS_PATH)
print_heading("Data and software versions")
print(f"Data repo '{get_repo_name(repo)}' @ {repo.commit().hexsha[:7]}")
print(f"dimcat version {dc.__version__}")
print(f"ms3 version {ms3.__version__}")

Data and software versions
--------------------------

Data repo 'romantic_piano_corpus' @ 52ef813
dimcat version 0.3.0
ms3 version 2.2.1

dataset = dc.Dataset()
dataset.load(directory=CORPUS_PATH, parse_tsv=False)

[annotated|all|default]
All corpora
-----------
View: This view is called 'annotated'. It 
	- excludes pieces that are not contained in the metadata,
	- filters out file extensions requiring conversion (such as .xml),
	- excludes review files and folders, and
	- includes only facets containing one of ['measures', 'notes$', 'expanded'].

                               has     active measures           notes        expanded       
                          metadata       view detected parsed detected parsed detected parsed
corpus                                                                                       
beethoven_piano_sonatas        yes  annotated       87     87       87     87       64     64
chopin_mazurkas                yes  annotated       55     55       55     55       55     55
debussy_suite_bergamasque      yes  annotated        4      4        4      4        4      4
dvorak_silhouettes             yes  annotated       12     12       12     12       12     12
grieg_lyric_pieces             yes  annotated       66     66       66     66       66     66
liszt_pelerinage               yes  annotated       19     19       19     19       19     19
medtner_tales                  yes  annotated       19     19       19     19       19     19
schumann_kinderszenen          yes  annotated       13     13       13     13       13     13
tchaikovsky_seasons            yes  annotated       12     12       12     12       12     12

9/12 facets are excluded from this view.


There are 1 orphans that could not be attributed to any of the respective corpus's pieces.

N = 287 annotated pieces, 838 parsed dataframes.

Metadata#

all_metadata = dataset.data.metadata()
print(f"Concatenated 'metadata.tsv' files cover {len(all_metadata)} of the {dataset.data.count_pieces()} scores.")
all_metadata.reset_index(level=1).groupby(level=0).nth(0).iloc[:,:20]

Concatenated 'metadata.tsv' files cover 287 of the 287 scores.
piece TimeSig KeySig last_mc last_mn length_qb last_mc_unfolded last_mn_unfolded length_qb_unfolded volta_mcs all_notes_qb n_onsets n_onset_positions guitar_chord_count form_label_count label_count annotated_key harmony_version annotators reviewers
corpus
beethoven_piano_sonatas 01-1 1: 2/2 1: -4 154 152 608.0 308.0 304.0 1216.0 1476.00 1679 985 0 0 241 f 2.3.0 Lars & Ya-Chuan (2.2.0), John Heilig (2.3.0) AN
chopin_mazurkas BI105-2op30-2 1: 3/4 1: 2 65 64 193.0 65.0 64.0 193.0 711.00 810 274 0 0 116 b 2.3.0 Wendelin Bitzan (1.0.0), Adrian Nagel (2.2.0),... JH, AN, DK
debussy_suite_bergamasque l075-01_suite_prelude 1: 4/4 1: -1 89 89 356.0 89.0 89.0 356.0 1533.67 1721 870 0 0 274 F 2.3.0 Adrian Nagel (2.1.1), Amelia Brey (2.3.0) AB, AN
dvorak_silhouettes op08n01 1: 6/8 1: 4, 7: -5, 49: 4 54 52 156.5 54.0 52.0 156.5 658.75 957 288 0 0 80 c# 2.3.0 Daniel Grote (2.1.1), Hanné Becker (2.3.0) Johannes Hentschel (2.1.1), AN
grieg_lyric_pieces op12n01 1: 2/4 1: -3 23 23 46.0 23.0 23.0 46.0 135.50 268 156 0 0 43 Eb 2.3.0 Adrian Nagel (2.1.1), John Heilig (2.30) Adrian Nagel
liszt_pelerinage 160.01_Chapelle_de_Guillaume_Tell 1: 4/4 1: 0 97 97 388.0 97.0 97.0 388.0 1902.42 2879 1069 0 0 174 C 2.3.0 Adrian Nagel (2.1.1), Amelia Brey (2.3.0) Johannes Hentschel (1-33 & 82-97), AB, AN
medtner_tales op08n01 1: 4/8 1: -3 81 81 162.0 81.0 81.0 162.0 603.00 1481 528 0 0 213 c 2.3.0 Wendelin Bitzan (2.2.0), John Heilig (2.3.0) Adrian Nagel, DK
schumann_kinderszenen n01 1: 2/4 1: 1 22 22 44.0 44.0 44.0 88.0 134.33 241 141 0 0 44 G 2.3.0 Tal Soker (2.1.1), John Heilig (2.3.0) AN, JHei, JH
tchaikovsky_seasons op37a01 1: 3/4 1: 3, 29: 1, 63: 3 103 103 309.0 103.0 103.0 309.0 1058.17 1537 829 0 0 313 A 2.3.0 Adrian Nagel (2.1.1), John Heilig (2.3.0) Johannes Hentschel, AN

Compute chronological order

chronological_order = chronological_corpus_order(all_metadata)
corpus_colors = dict(zip(chronological_order, CORPUS_COLOR_SCALE))
chronological_order

['beethoven_piano_sonatas',
 'chopin_mazurkas',
 'schumann_kinderszenen',
 'liszt_pelerinage',
 'tchaikovsky_seasons',
 'dvorak_silhouettes',
 'grieg_lyric_pieces',
 'debussy_suite_bergamasque',
 'medtner_tales']

all_notes = dataset.data.get_all_parsed('notes', force=True, flat=True)
print(f"{len(all_notes.index)} notes over {len(all_notes.groupby(level=[0,1]))} files.")
all_notes.head()

488892 notes over 287 files.
mc mn quarterbeats quarterbeats_all_endings duration_qb mc_onset mn_onset timesig staff voice ... nominal_duration scalar tied tpc midi name octave chord_id volta tremolo
corpus piece i
beethoven_piano_sonatas 01-1 0 1 0 0 0 1.0 0 3/4 2/2 1 1 ... 1/4 1 <NA> 0 60 C4 4 0 <NA> NaN
1 2 1 1 1 1.0 0 0 2/2 1 1 ... 1/4 1 <NA> -1 65 F4 4 1 <NA> NaN
2 2 1 2 2 1.0 1/4 1/4 2/2 1 1 ... 1/4 1 <NA> -4 68 Ab4 4 2 <NA> NaN
3 2 1 3 3 1.0 1/2 1/2 2/2 1 1 ... 1/4 1 <NA> 0 72 C5 5 3 <NA> NaN
4 2 1 4 4 1.0 3/4 3/4 2/2 1 1 ... 1/4 1 <NA> -1 77 F5 5 4 <NA> NaN

5 rows × 22 columns

def weight_notes(nl, group_col='midi', precise=True):
    summed_durations = nl.groupby(group_col).duration_qb.sum()
    shortest_duration = summed_durations[summed_durations > 0].min()
    summed_durations /= shortest_duration # normalize such that the shortest duration results in 1 occurrence
    if not precise:
        # This simple trick reduces compute time but also precision:
        # The rationale is to have the smallest value be slightly larger than 0.5 because
        # if it was exactly 0.5 it would be rounded down by repeat_notes_according_to_weights()
        summed_durations /= 1.9999999
    return repeat_notes_according_to_weights(summed_durations)
    
def repeat_notes_according_to_weights(weights):
    try:
        counts = weights.round().astype(int)
    except Exception:
        return pd.Series(dtype=int)
    counts_reflecting_weights = []
    for pitch, count in counts.items():
        counts_reflecting_weights.extend([pitch]*count)
    return pd.Series(counts_reflecting_weights)

Ambitus#

corpus_names = {corp: get_corpus_display_name(corp) for corp in chronological_order}
chronological_corpus_names = list(corpus_names.values())
corpus_name_colors = {corpus_names[corp]: color for corp, color in corpus_colors.items()}
all_notes['corpus_name'] = all_notes.index.get_level_values(0).map(corpus_names)

grouped_notes = all_notes.groupby('corpus_name')
weighted_midi = pd.concat([weight_notes(nl, 'midi', precise=False) for _, nl in grouped_notes], keys=grouped_notes.groups.keys()).reset_index(level=0)
weighted_midi.columns = ['dataset', 'midi']
weighted_midi
dataset midi
0 Beethoven Sonatas 24
1 Beethoven Sonatas 24
2 Beethoven Sonatas 24
3 Beethoven Sonatas 24
4 Beethoven Sonatas 24
... ... ...
13639 Tchaikovsky Seasons 91
13640 Tchaikovsky Seasons 91
13641 Tchaikovsky Seasons 92
13642 Tchaikovsky Seasons 92
13643 Tchaikovsky Seasons 93

623526 rows × 2 columns

yaxis=dict(tickmode= 'array',
           tickvals= [12, 24, 36, 48, 60, 72, 84, 96],
           ticktext = ["C0", "C1", "C2", "C3", "C4", "C5", "C6", "C7"],
           gridcolor='lightgrey',
           )
fig = px.violin(weighted_midi, 
                x='dataset', 
                y='midi', 
                color='dataset', 
                box=True,
                labels=dict(
                    dataset='',
                    midi='distribution of pitches by duration'
                ),
                category_orders=dict(dataset=chronological_corpus_names),
                color_discrete_map=corpus_name_colors,
                width=1000, height=600,
               )
fig.update_traces(spanmode='hard') # do not extend beyond outliers
fig.update_layout(yaxis=yaxis, 
                  **STD_LAYOUT,
                 showlegend=False)
fig.show()

Tonal Pitch Classes (TPC)#

weighted_tpc = pd.concat([weight_notes(nl, 'tpc') for _, nl in grouped_notes], keys=grouped_notes.groups.keys()).reset_index(level=0)
weighted_tpc.columns = ['dataset', 'tpc']
weighted_tpc
dataset tpc
0 Beethoven Sonatas -10
1 Beethoven Sonatas -9
2 Beethoven Sonatas -9
3 Beethoven Sonatas -9
4 Beethoven Sonatas -9
... ... ...
27284 Tchaikovsky Seasons 14
27285 Tchaikovsky Seasons 14
27286 Tchaikovsky Seasons 14
27287 Tchaikovsky Seasons 14
27288 Tchaikovsky Seasons 15

428617 rows × 2 columns

As violin plot#

yaxis=dict(
    tickmode= 'array',
    tickvals= [-12, -9, -6, -3, 0, 3, 6, 9, 12, 15, 18],
    ticktext = ["Dbb", "Bbb", "Gb", "Eb", "C", "A", "F#", "D#", "B#", "G##", "E##"],
    gridcolor='lightgrey',
    zerolinecolor='lightgrey',
    zeroline=True
           )
fig = px.violin(weighted_tpc, 
                x='dataset', 
                y='tpc', 
                color='dataset', 
                box=True,
                labels=dict(
                    dataset='',
                    tpc='distribution of tonal pitch classes by duration'
                ),
                category_orders=dict(dataset=chronological_corpus_names),
                color_discrete_map=corpus_name_colors,
                width=1000, 
                height=600,
               )
fig.update_traces(spanmode='hard') # do not extend beyond outliers
fig.update_layout(yaxis=yaxis, 
                  **STD_LAYOUT,
                 showlegend=False)
fig.show()

As bar plots#

bar_data = all_notes.groupby('tpc').duration_qb.sum().reset_index()
x_values = list(range(bar_data.tpc.min(), bar_data.tpc.max()+1))
x_names = ms3.fifths2name(x_values)
fig = px.bar(bar_data, x='tpc', y='duration_qb',
             labels=dict(tpc='Named pitch class',
                             duration_qb='Duration in quarter notes'
                            ),
             color_discrete_sequence=CORPUS_COLOR_SCALE,
             width=1000, height=300,
             )
fig.update_layout(**STD_LAYOUT)
fig.update_yaxes(gridcolor='lightgrey')
fig.update_xaxes(gridcolor='lightgrey', zerolinecolor='grey', tickmode='array', 
                 tickvals=x_values, ticktext = x_names, dtick=1, ticks='outside', tickcolor='black', 
                 minor=dict(dtick=6, gridcolor='grey', showgrid=True),
                )
fig.show()

scatter_data = all_notes.groupby(['corpus_name', 'tpc']).duration_qb.sum().reset_index()
fig = px.bar(scatter_data, x='tpc', y='duration_qb', color='corpus_name', 
                 labels=dict(
                     duration_qb='duration',
                     tpc='named pitch class',
                 ),
                 category_orders=dict(dataset=chronological_corpus_names),
                 color_discrete_map=corpus_name_colors,
                 width=1000, height=500,
                )
fig.update_layout(**STD_LAYOUT)
fig.update_yaxes(gridcolor='lightgrey')
fig.update_xaxes(gridcolor='lightgrey', zerolinecolor='grey', tickmode='array', 
                 tickvals=x_values, ticktext = x_names, dtick=1, ticks='outside', tickcolor='black', 
                 minor=dict(dtick=6, gridcolor='grey', showgrid=True),
                )
fig.show()

As scatter plots#

fig = px.scatter(scatter_data, x='tpc', y='duration_qb', color='corpus_name', 
                 labels=dict(
                     duration_qb='duration',
                     tpc='named pitch class',
                 ),
                 category_orders=dict(dataset=chronological_corpus_names),
                 color_discrete_map=corpus_name_colors,
                 facet_col='corpus_name', facet_col_wrap=3, facet_col_spacing=0.03,
                 width=1000, height=1000,
                )
fig.update_traces(mode='lines+markers')
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.update_layout(**STD_LAYOUT, showlegend=False)
fig.update_xaxes(gridcolor='lightgrey', zerolinecolor='lightgrey', tickmode='array', tickvals= [-12, -6, 0, 6, 12, 18],
    ticktext = ["Dbb", "Gb", "C", "F#", "B#", "E##"], visible=True, )
fig.update_yaxes(gridcolor='lightgrey', zeroline=False, matches=None, showticklabels=True)
fig.show()

no_accidental = bar_data[bar_data.tpc.between(-1,5)].duration_qb.sum()
with_accidental = bar_data[~bar_data.tpc.between(-1,5)].duration_qb.sum()

entire = no_accidental + with_accidental
f"Fraction of note duration without accidental of the entire durations: {no_accidental} / {entire} = {no_accidental / entire}"

'Fraction of note duration without accidental of the entire durations: 199470.75073438467 / 323946.72040343913 = 0.6157517214126025'

Notes and staves#

print("Distribution of notes over staves:")
value_count_df(all_notes.staff)

Distribution of notes over staves:
counts %
staff
1 259435 0.530659
2 226076 0.462425
3 2397 0.004903
4 984 0.002013